Enterprise AI Analysis
Benchmarking Unlearning for Vision Transformers
Kairan Zhao, Iurie Luca, Peter Triantafillou
Publication Date: February 24, 2026
Research in machine unlearning (MU) has gained strong momentum: MU is now widely regarded as a critical capability for building safe and fair AI. In parallel, research into transformer architectures for computer vision tasks has been highly successful: Increasingly, Vision Transformers (VTs) emerge as strong alternatives to CNNs. Yet, MU research for vision tasks has largely centered on CNNs, not VTs. While benchmarking MU efforts have addressed LLMs, diffusion models, and CNNs, none exist for VTs. This work is the first to attempt this, benchmarking MU algorithm performance in different VT families (ViT and Swin-T) and at different capacities. The work employs (i) different datasets, selected to assess the impacts of dataset scale and complexity; (ii) different MU algorithms, selected to represent fundamentally different approaches for MU; and (iii) both single-shot and continual unlearning protocols. Additionally, it focuses on benchmarking MU algorithms that leverage training data memorization, since leveraging memorization has been recently discovered to significantly improve the performance of previously SOTA algorithms. En route, the work characterizes how VTs memorize training data relative to CNNs, and assesses the impact of different memorization proxies on performance. The benchmark uses unified evaluation metrics that capture two complementary notions of forget quality along with accuracy on unseen (test) data and on retained data. Overall, this work offers a benchmarking basis, enabling reproducible, fair, and comprehensive comparisons of existing (and future) MU algorithms on VTs. And, for the first time, it sheds light on how well existing algorithms work in VT settings, establishing a promising reference performance baseline.
Executive Summary
This paper presents the first comprehensive benchmark for Machine Unlearning (MU) in Vision Transformers (VTs). It evaluates MU algorithm performance across different VT architectures (ViT, Swin-T), capacities, and datasets, focusing on how VTs memorize data and the effectiveness of CNN-derived memorization proxies. The study introduces unified metrics (ToW, ToW-MIA) for evaluation and highlights that existing SOTA MU algorithms for CNNs can be effective in VTs, with NegGrad+ emerging as a robust performer. It also provides insights into architecture-method compatibility and the impact of pretraining and continual unlearning on VTs. This work establishes a baseline for future MU research in VTs, emphasizing the importance of responsible AI development.
0.975
Average ToW (CIFAR-100, HR, NegGrad+)
0.902
Average ToW-MIA (CIFAR-100, HR, NegGrad+)
-0.90
Swin-T Memory Correlation (Confidence)
Deep Analysis & Enterprise Applications
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Vision Transformers (VTs)
Vision Transformers are highly effective architectures for computer vision, leveraging self-attention mechanisms to process images. Unlike CNNs, VTs lack strong spatial inductive biases, making them data-hungry and often requiring pretrain-then-finetune regimes. Their global attention mechanisms lead to more diffuse parameter involvement compared to CNNs.
VTs vs. CNNs: Similar Memorization Patterns
Despite architectural differences, VTs exhibit fundamentally similar memorization behaviors to CNNs, especially on complex datasets like CIFAR-100. On simpler tasks (CIFAR-10), VTs show slightly lower memorization due to pretraining and global attention.
| Feature | ViT (e.g., ViT-Small) | Swin-T (e.g., Swin-Tiny) |
|---|---|---|
| Attention Mechanism | Global self-attention on image patches | Shifted-window self-attention with hierarchical representations |
| Inductive Biases | Learns structure from data, less spatial bias | Introduces locality and hierarchical structure (more CNN-like) |
| Parameter Involvement | More diffuse parameter involvement | More concentrated, targeted unlearning |
| Preferred MU Method (HR Proxy) | Fine-tune | NegGrad+ |
| Performance on Complex Data | Can struggle on harder datasets (e.g., CIFAR-100) without specific optimizations | Outperforms ViT on more complex datasets |
Machine Unlearning Algorithms (MU)
Machine unlearning aims to remove the influence of specific problematic data from trained models. Recent advancements highlight memorization as a key factor for effective MU. The study evaluates Fine-tune, NegGrad+, and SalUn algorithms, integrated within the RUM framework.
RUM Framework for Unlearning
Refinement: Partition forget set by memorization
→
Matching: Select tailored unlearning methods
→
Unlearning: Apply algorithm sequentially on partitions
NegGrad+ is the Most Robust MU Method for VTs
NegGrad+ (especially with Holdout Retraining proxy) consistently performs strongly across all datasets and architectures, proving robust for both simple and complex unlearning tasks in VTs.
| Algorithm | Key Characteristic | Performance on VTs (CIFAR-100, HR) |
|---|---|---|
| Fine-tune | Fine-tunes on retained data only | Surprisingly effective, especially for ViT and simpler datasets |
| NegGrad+ | Gradient ascent on forget set, descent on retain set | Consistently strong, robust, excels with Holdout Retraining on complex data (e.g., Swin-T) |
| SalUn | Parameter-selective unlearning based on saliency | Good ToW but struggles with ToW-MIA on complex datasets; unreliable for privacy-sensitive settings in VTs |
Memorization & Proxies
Memorization quantifies a model's dependency on specific examples. Efficient proxies are crucial for estimating memorization without expensive retraining. This work assesses CNN-derived proxies for their validity in VTs.
Confidence & Holdout Retraining are Effective Proxies for VTs
Confidence consistently shows the strongest negative correlation (-0.79 to -0.91) with true memorization, similar to CNNs. Holdout Retraining offers moderate but significant positive correlation and large computational advantages, making both valuable for VTs.
| Proxy | Description | Spearman Correlation (CIFAR-100, Swin-T) | Computational Advantage |
|---|---|---|---|
| Confidence | Model's prediction confidence for correct label | -0.90 | Low |
| Max Confidence | Max probability across all classes | -0.86 | Low |
| Entropy | Entropy of predicted probabilities | -0.82 | Low |
| Binary Accuracy | Classifier accuracy on training/out-of-training | -0.78 | Low |
| Holdout Retraining | KL divergence between full and holdout model predictions | +0.52 | High (No retraining during proxy calculation) |
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Business Implications of Responsible AI in VTs
Enhanced AI Safety & Ethics: Implementing effective unlearning in VTs is crucial for addressing 'right to be forgotten' requirements and mitigating risks from biased, erroneous, or privacy-sensitive data, making AI systems more trustworthy.
Optimized Model Management: The benchmark identifies optimal unlearning strategies for different VT architectures and datasets, guiding enterprises in efficiently managing and updating their vision AI models without compromising performance.
Reduced Operational Overhead: Leveraging efficient memorization proxies like Holdout Retraining significantly reduces the computational cost of unlearning, enabling scalable and practical deployment of responsible AI in real-world scenarios.
Strategic Architecture Choices: Insights into architecture-method compatibility (e.g., ViT with Fine-tune, Swin-T with NegGrad+) allow businesses to select or adapt VT models that are inherently more amenable to unlearning, streamlining development and compliance efforts.
Your Implementation Roadmap
Our phased approach ensures a smooth, effective, and compliant integration of Machine Unlearning into your Vision Transformer workflows.
Phase 1: Initial Assessment & Strategy
Duration: 1-2 Weeks
Evaluate existing VT models, identify critical data types requiring unlearning, and define specific unlearning objectives. Select appropriate MU algorithms and memorization proxies based on architectural compatibility and dataset complexity.
Phase 2: Pilot Implementation & Benchmarking
Duration: 3-5 Weeks
Implement selected MU algorithms (e.g., NegGrad+ with HR for Swin-T) on a representative subset of VTs and datasets. Conduct initial benchmarking using ToW and ToW-MIA metrics to establish a performance baseline.
Phase 3: Iterative Refinement & Integration
Duration: 6-8 Weeks
Refine hyperparameters and strategies based on pilot results. Integrate continuous unlearning protocols, ensuring stability and minimal performance degradation over time. Develop monitoring tools for unlearning efficacy and privacy protection.
Phase 4: Scalable Deployment & Compliance
Duration: Ongoing
Roll out unlearning capabilities across production VT models. Establish robust processes for data removal requests and compliance with privacy regulations. Continuously monitor model behavior for unlearning quality and overall performance.
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